Thinking Inside the box With AI and ML

A New Kind of 'Insider Intelligence'

Mary Beth Massat; McKenna Bryant

Disclosures

Appl Radiol. 2019;48(1):34-37. 

In This Article

Abstract and Introduction

Introduction

The healthcare artificial intelligence (AI) market is projected to reach $6.6 billion in 2021, with AI applications potentially creating up to $150 billion in annual savings for the U.S. healthcare economy by 2026. It's no wonder that 39% of healthcare provider executives are planning to invest in AI, machine learning (ML), and predictive analytics.

There are more options than ever, as healthcare companies embed AI in diagnostic imaging systems to streamline the data capture and analysis process, with dramatic implications for some of society's most pressing medical challenges.

"There has been a remarkable embrace of everything and anything AI related to imaging and beyond," says Rasu Shrestha, MD, MBA, Chief Innovation Officer for the University of Pittsburgh Medical Center (UPMC) and Executive Vice President of UPMC Enterprises, both in Pittsburgh, PA. "Right now, we are seeing the peak of the hype cycle with AI, to the point that many companies had something on AI to showcase in their booth at this past RSNA. It is important, however, that we are careful to not throw everything into the AI or machine learning bucket."

He adds that it's also important to understand the specific needs within workflow and around the augmentation of human capabilities utilizing technological advancements that are being made in AI and ML. The use of AI in imaging modalities could assist with quality issues related to imaging, particularly in the use of contrast, and could help with identifying appropriateness of the study and guide image capture on a modality.

"As we move to value-based imaging, we continually try to optimize the quality of the images that we are capturing," he explains. "AI has the ability to help intellectually guide us through the best ways to capture studies for the right subjects, whether that be an obese patient, a pediatric patient or anyone else."

However, it is important for the industry to take a step back, perhaps hit the AI reset button, and consider the broader implications of embracing AI, Dr. Shrestha says. He suggests there is a need for more data science training embedded into the curriculum of medical schools, residencies and fellowships, as well as more focus on delivery mechanisms and workflows in clinical practice…and not just on an application.

Dr. Shrestha believes that AI will help advance some of the broader strategies within healthcare, such as personalizing an individual's care or quantifying the value of a specific task, such as segmenting and tracking a lesion over time and then correlating those actions to improved patient outcomes.

"AI will help us get to personalized medicine sooner, but it is not the silver bullet," Dr. Shrestha cautions. "It is really important to understand that AI, like anything else, is an enabler. How we capitalize on it to achieve our broader ambitions and goals is very important."

The other element of AI as an enabler to personalizing care is to shift the conversation and focus from utility to experiential. Dr. Shrestha often discusses the user interface and the user experience being of paramount importance, perhaps even more important than the algorithms on the back end.

"If we go that extra mile, and the app that is created ties to that broader context of value generation or value quantification, then we are moving beyond where we are right now, which is the proliferation of AI as point solutions across the board," Dr. Shrestha says. "I urge us all to think more broadly and more holistically on how AI can impact workflow and truly add value to the overall strategies that we are developing in our imaging departments and healthcare facilities."

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